class_docstr: This is a configurable module to get a single device to train model on.
  It can pick up CUDA devices and it will fall back to CPU if they are not available.
  
  It has other small advantages such as being able to view the
  actual device name on configurations view of
  `labml app <https://github.com/labmlai/labml/tree/master/app>`_
  
  Arguments:
      cuda_device (int): The CUDA device number. Defaults to ``0``.
      use_cuda (bool): Whether to use CUDA devices. Defaults to ``True``.
  
  `Here's an example usage <https://github.com/labmlai/labml/blob/master/samples/pytorch/mnist/e_labml_helpers.py>`_.
code: def __init__(self):
      super().__init__(_primary="device")
  
file_path: labml/helpers/labml_helpers/device.py
imports: import torch
  from labml.configs import BaseConfigs, hyperparams, option
label_desc: Use image node_id 1 to create a new DeviceConfigs object from inherited base classes: BaseConfigs with example: obj = DeviceConfigs()